Fight ill-posedness with ill-posedness: Single-shot variational depth super-resolution from shading

Bjoern Haefner (1) , Yvain Quéau (1) , Thomas Moellenhoff (1) , Daniel Cremers (1)

1 TUM - Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne]

We put forward a principled variational approach for up-sampling a single depth map to the resolution of the companion color image provided by an RGB-D sensor. We combine heterogeneous depth and color data in order to jointly solve the ill-posed depth super-resolution and shape-from-shading problems. The low-frequency geometric information necessary to disambiguate shape-from-shading is extracted from the low-resolution depth measurements and, symmetrically, the high-resolution photometric clues in the RGB image provide the high-frequency information required to disambiguate depth super-resolution.

Domains: Computer Science [cs]; Computer Vision and Pattern Recognition [cs.CV]

Bjoern Haefner, Yvain Quéau, Thomas Moellenhoff, Daniel Cremers. Fight ill-posedness with ill-posedness: Single-shot variational depth super-resolution from shading. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2018, Salt Lake City, United States. pp.164-174, ⟨10.1109/CVPR.2018.00025⟩. ⟨hal-02118545⟩